Reich funded by the Gates Foundation

A spatiotemporal recommendation engine for malaria control

In collaboration with researchers from UNC, we will develop and disseminate a recommender system for spatiotemporal resource allocation to maximize the efficacy of malaria control efforts in the Democratic Republic of Congo. The malaria research community has made great strides in mapping disease prevalence, modeling its transmission, developing and testing effective interventions such as bed nets, and implementing these interventions in practice. We propose to build on this work to develop a real-time recommendation engine for precision interventions to help policy-makers decide how to allocate their limited resources. Our hypothesis is that allocating resources following the proposed recommendation engine will substantially improve the effectiveness of malaria suppression efforts in the DRC.